The Dremio Blog

The New Economics of Data Cloud data warehouses like Amazon Redshift were built for a world that no longer exists. In that earlier era, organizations focused primarily on structured business intelligence, static dashboards, and predictable workloads. Data was tightly controlled, compute resources were fixed, and dynamic scalability for rapidly changing workloads was not a concern. […]

Running Polaris locally with MinIO gives you a hands-on view of how an open catalog governs Iceberg tables. But when it’s time to move beyond experimentation, deploying and managing a full Polaris environment can take more effort. That’s where Dremio’s integrated catalog comes in. Dremio’s catalog is built directly on Apache Polaris. It delivers all the same open governance, authentication, and interoperability while removing the setup work. You get a production-ready Polaris deployment with a complete user interface, automated security management, and fine-grained access controls, all without maintaining separate services.

Caching dramatically reduces latency and computational costs by storing frequently accessed data closer to where it's needed. Instead of repeated expensive operations - such as fetching from object storage, planning complex queries, or executing SQL - the data you need is provided in fast, local memory. To deliver on this, Dremio implements different layers of […]

The Shift from Warehouses to the Agentic Lakehouse Amazon Redshift has long been a dependable data warehouse for analytics, but the analytics landscape has evolved. Organizations are no longer just running dashboards—they’re powering agentic AI systems that reason, act, and make autonomous decisions based on live business data. These workloads demand real-time responses, high concurrency, […]

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Performance management for Apache Iceberg tables isn’t just about cleaning up small files, it’s about ensuring your data layout evolves in step with your ingestion patterns and query workloads. Dremio’s OPTIMIZE command provides the precision engineers need: merging, splitting, and reclustering data into efficient layouts while keeping metadata lean. With its flexible parameters, you can tailor compaction jobs to strike the right balance between optimization depth, runtime, and cost. At the same time, Dremio’s auto-optimization features mean you don’t always have to run these jobs manually. By letting Dremio continuously monitor and optimize Iceberg tables in the background, your most critical datasets stay query-ready without the overhead of constant maintenance.

The real secret to minimizing Iceberg table maintenance isn’t running more optimization jobs, it’s writing smarter data from the very beginning. By combining batch and streaming ingestion best practices, designing thoughtful partitioning and clustering strategies, tuning table properties, and monitoring file health, you can dramatically reduce the frequency and cost of downstream operations like OPTIMIZE.

Apache Iceberg’s snapshot model is a game-changer for time travel, auditing, and recovery, but it comes with a responsibility: old data must be managed carefully. Without proactive cleanup, tables can accumulate unnecessary files, driving up storage costs, slowing queries, and even creating compliance risks. Dremio’s VACUUM TABLE command provides the control data engineers and architects need to: Expire outdated snapshots, keeping only the versions that align with retention policies. Permanently remove deleted data to meet GDPR and CCPA requirements. Clean up orphan files to ensure storage remains lean and predictable.

Overview Dremio provides out-of-the-box methods of handling complex data types in, for example JSON and parquet datasets. Common characteristics are embedded “columns within columns” and “rows within columns”.  In this blog, we will demonstrate how Dremio can discover and handle these types of data. The examples have been tested on the following Dremio versions: Preparation […]

Agentic AI is an artificial intelligence system that is designed to operate autonomously. With minimal human supervision it can be expected to make decisions and perform tasks with specifically trained agents. This is thanks in large part to Large Language Models (LLMs) which provide agentic AI with enhanced reasoning and the ability to understand context. […]

This is exactly where MCP and A2A come together. MCP ensures that agents can securely interact with enterprise tools like Dremio, accessing trusted data through well-defined interfaces. A2A, in turn, provides the framework for those agents to collaborate, delegating tasks, exchanging results, and orchestrating end-to-end workflows.

Regulatory change in the food and agriculture supply chain is no longer about periodic paperwork, it’s about maintaining continuous, trustworthy, and accessible data. Whether it’s proving product lineage within 24 hours for the FDA, reporting Scope 3 emissions under the EU’s CSRD, or ensuring supplier compliance through due diligence laws, the common denominator is data complexity.

As education becomes increasingly data-driven, the stakes for protecting sensitive information have never been higher. Regulations like FERPA, COPPA, GDPR, and state-level privacy laws demand rigorous compliance, while rising cyber threats highlight the urgent need for robust security and governance. At the same time, educators and edtech companies cannot afford to sacrifice innovation, students expect personalized learning, administrators need real-time insights, and institutions are exploring AI-driven opportunities to improve outcomes.

The transportation and automotive industry sits at a unique inflection point: vehicles are now data platforms as much as they are machines. With each car, truck, or bus generating gigabytes of telemetry, location, and sensor data every hour, the potential for innovation is enormous. Yet the regulatory landscape, from U.S. privacy laws to EU mandates on data portability and global cybersecurity standards, makes it clear that this data must be handled with care. Non-compliance isn’t just a legal risk; it undermines consumer trust and can derail new business models before they gain traction.

Telecom companies cannot afford to rely on platforms that “might” meet regulatory standards ,  they need proven, auditable compliance. Dremio is designed with security and certification at its core, giving providers confidence that their data practices align with industry and legal obligations.